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Location and orientation united graph comparison for topographic point cloud change estimation
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 10.6 ) Pub Date : 2024-11-29 , DOI: 10.1016/j.isprsjprs.2024.11.016
Shoujun Jia, Lotte de Vugt, Andreas Mayr, Chun Liu, Martin Rutzinger

3D topographic point cloud change estimation produces fundamental inputs for understanding Earth surface process dynamics. In general, change estimation aims at detecting the largest possible number of points with significance (i.e., difference > uncertainty) and quantifying multiple types of topographic changes. However, several complex factors, including the inhomogeneous nature of point cloud data, the high uncertainty in positional changes, and the different types of quantifying difference, pose challenges for the reliable detection and quantification of 3D topographic changes. To address these limitations, the paper proposes a graph comparison-based method to estimate 3D topographic change from point clouds. First, a graph with both location and orientation representation is designed to aggregate local neighbors of topographic point clouds against the disordered and unstructured data nature. Second, the corresponding graphs between two topographic point clouds are identified and compared to quantify the differences and associated uncertainties in both location and orientation features. Particularly, the proposed method unites the significant changes derived from both features (i.e., location and orientation) and captures the location difference (i.e., distance) and the orientation difference (i.e., rotation) for each point with significant change. We tested the proposed method in a mountain region (Sellrain, Tyrol, Austria) covered by three airborne laser scanning point cloud pairs with different point densities and complex topographic changes at intervals of four, six, and ten years. Our method detected significant changes in 91.39 % − 93.03 % of the study area, while a state-of-the-art method (i.e., Multiscale Model-to-Model Cloud Comparison, M3C2) identified 36.81 % − 47.41 % significant changes for the same area. Especially for unchanged building roofs, our method measured lower change magnitudes than M3C2. Looking at the case of shallow landslides, our method identified 84 out of a total of 88 reference landslides by analysing change in distance or rotation. Therefore, our method not only detects a large number of significant changes but also quantifies two types of topographic changes (i.e., distance and rotation), and is more robust against registration errors. It shows large potential for estimation and interpretation of topographic changes in natural environments.

中文翻译:


位置和方向统一图比较,用于地形点云变化估计



3D 地形点云变化估计为理解地球表面过程动力学提供了基本输入。一般来说,变化估计旨在检测具有显著性的尽可能多的点(即差异 > 不确定性)并量化多种类型的地形变化。然而,一些复杂的因素,包括点云数据的不均匀性、位置变化的高度不确定性以及不同类型的量化差异,对 3D 地形变化的可靠检测和量化构成了挑战。为了解决这些限制,本文提出了一种基于图形比较的方法,用于估计点云的 3D 地形变化。首先,设计了一个同时具有位置和方向表示的图形,以针对无序和非结构化数据性质聚合地形点云的局部邻居。其次,识别并比较两个地形点云之间的相应图表,以量化位置和方向特征的差异和相关不确定性。特别是,所提出的方法将两个特征(即位置和方向)的显着变化联合起来,并捕获每个具有显着变化的点的位置差异(即距离)和方向差异(即旋转)。我们在山区(奥地利蒂罗尔州塞尔兰)测试了所提出的方法,该山区被三个具有不同点密度和复杂地形变化的机载激光扫描点云对覆盖,间隔为 4 年、6 年和 10 年。我们的方法检测到 91.39 % − 93.03 % 的研究区域发生了显著变化,而最先进的方法(即,多尺度模型到模型云比较,M3C2)确定了相同区域 36.81% − 47.41% 的显著变化。特别是对于不变的建筑物屋顶,我们的方法测得的变化幅度低于 M3C2。以浅层滑坡为例,我们的方法通过分析距离或旋转的变化,确定了总共 88 个参考滑坡中的 84 个。因此,我们的方法不仅检测到大量重大变化,还量化了两种类型的地形变化(即距离和旋转),并且对配准误差具有更强的抵抗力。它显示了估计和解释自然环境中地形变化的巨大潜力。
更新日期:2024-11-29
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